Multi-level index for global and partial content-based image retrieval

This article presents a quadtree-based data structure for effective indexing of images. An image is represented by a multi-level feature vector, computed by a recursive decomposition of the image into four quadrants and stored as a full fixed-depth balanced quadtree. A node of the quadtree stores a feature vector of the corresponding image quadrant. A more general quadtree-based structure called QUIP-tree (QUadtree-based Index for image retrieval and Pattern search) is used to index the multi-level feature vectors of the images and their quadrants. A QUIP-tree node is an entry to a set of clusters that groups similar quadrants according to some pre-defined distances. The QUIP-tree allows a multi-level filtering in content-based image retrieval as well as partial queries on images.

[1]  Christos Faloutsos,et al.  Efficient and effective Querying by Image Content , 1994, Journal of Intelligent Information Systems.

[2]  Avinash C. Kak,et al.  Content-based image retrieval from large medical databases , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[3]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[4]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[5]  Aidong Zhang,et al.  Supporting Content-Based Retrieval in Large Image Database Systems , 1997, Multimedia Tools and Applications.

[6]  N. Suematsu,et al.  Region-Based Image Retrieval using Wavelet Transform , 2002 .

[7]  Amarnath Gupta,et al.  Visual information retrieval , 1997, CACM.

[8]  Forouzan Golshani,et al.  Proceedings of the Eighth International Conference on Data Engineering , 1992 .

[9]  Yannis Manolopoulos,et al.  Spatial Databases , 2004 .

[10]  Carla E. Brodley,et al.  Local versus global features for content-based image retrieval , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[11]  Mario A. Nascimento,et al.  Content-based sub-image retrieval using relevance feedback , 2004, MMDB '04.

[12]  Nicu Sebe,et al.  Multi-scale sub-image search , 1999, MULTIMEDIA '99.

[13]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Yannis Manolopoulos.  Spatial Databases: Technologies, Techniques and Trends , 2005, Spatial Databases.

[15]  Nicu Sebe,et al.  Evaluation of Salient Point Techniques , 2002, CIVR.

[16]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[17]  Ilaria Bartolini,et al.  The PIBE personalizable image browsing engine , 2004, CVDB '04.

[18]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[19]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[20]  Geneviève Jomier,et al.  Quadtree-Based Image Representation and Retrieval , 2005, Spatial Databases.

[21]  Jan Rittinger,et al.  Efficient and Effective Querying by Image Content , 2004 .

[22]  Sharad Mehrotra,et al.  Similar shape retrieval in MARS , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[24]  Hae-Kwang Kim,et al.  Region-based shape descriptor invariant to rotation, scale and translation , 2000, Signal Process. Image Commun..

[25]  Beng Chin Ooi,et al.  Efficient Image Retrieval By Color Contents , 1994, ADB.

[26]  Sergey Brin,et al.  Near Neighbor Search in Large Metric Spaces , 1995, VLDB.

[27]  Shu Lin,et al.  An Extendible Hash for Multi-Precision Similarity Querying of Image Databases , 2001, VLDB.

[28]  Ashfaq A. Khokhar,et al.  Quantized CIELab* space and encoded spatial structure for scalable indexing of large color image archives , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[29]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[30]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[31]  Nozha Boujemaa,et al.  Region Queries without Segmentation for Image Retrieval by Content , 1999, VISUAL.

[32]  Jack A. Ladson.  Colour image science—Exploiting digital media , 2003 .

[33]  Alexander Thomasian,et al.  CSVD: Clustering and Singular Value Decomposition for Approximate Similarity Search in High-Dimensional Spaces , 2003, IEEE Trans. Knowl. Data Eng..

[34]  William I. Grosky,et al.  Indexing and retrieval of images by spatial constraints , 2003, J. Vis. Commun. Image Represent..

[35]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[36]  Geneviève Jomier,et al.  Distances de similarité d'images basées sur les arbres quaternaires , 2002, BDA.

[37]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[38]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[39]  Ronald Fagin,et al.  Extendible hashing—a fast access method for dynamic files , 1979, ACM Trans. Database Syst..